Performance Analysis of Different Operators in Genetic Algorithm for Solving Continuous and Discrete Optimization Problems

Shilun Song, Hu Jin, Qiang Yang

Abstract

Genetic algorithm (GA), as a powerful meta-heuristics algorithm, has broad applicability to different optimization problems. Although there are many researches about GA, few works have been done to synthetically summarize the impact of different genetic operators and different parameter settings on GA. To fill this gap, this paper has conducted extensive experiments on GA to investigate the influence of different operators and parameter settings in solving both continuous and discrete optimizations. Experiments on 16 nonlinear optimization (NLO) problems and 9 traveling salesman problems (TSP) show that tournament selection, uniform crossover, and a novel combination-based mutation are the best choice for continuous problems, while roulette wheel selection, distance preserving crossover, and swapping mutation are the best choices for discrete problems. It is expected that this work provides valuable suggestions for users and new learners.

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Paper Citation


in Harvard Style

Song S., Jin H. and Yang Q. (2021). Performance Analysis of Different Operators in Genetic Algorithm for Solving Continuous and Discrete Optimization Problems. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-509-8, pages 536-547. DOI: 10.5220/0010494005360547


in Bibtex Style

@conference{iceis21,
author={Shilun Song and Hu Jin and Qiang Yang},
title={Performance Analysis of Different Operators in Genetic Algorithm for Solving Continuous and Discrete Optimization Problems},
booktitle={Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2021},
pages={536-547},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010494005360547},
isbn={978-989-758-509-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Performance Analysis of Different Operators in Genetic Algorithm for Solving Continuous and Discrete Optimization Problems
SN - 978-989-758-509-8
AU - Song S.
AU - Jin H.
AU - Yang Q.
PY - 2021
SP - 536
EP - 547
DO - 10.5220/0010494005360547